Imagine a world 8 years from now where instead of a warehouse, Amazon is a factory. Products are made in small quantities, as needed, based on direct input from users to designers and developers. In this world design directly drives product creation, and data informs design. Consumer products are made locally, with local materials and workers, while at the same time being able to use design and engineering talent from anywhere on earth. It simultaneously looks exactly like our world, but is totally different.

First, let me tell you a bit about my background. I ’m a user experience designer. I was one of the first professional Web designers in 1993, where I was lucky enough to be present for the birth of such things as the online shopping cart and the search engine. This is the navigation for a hot sauce shopping site I designed in 1994.

I’m proud of the fact that 16 years later they were still using the same visual identity. These were some of the oldest pixels on the Web.

Here’s one of my UI designs for the advanced search for HotBot, an early search engine, from 1997. If you’re wondering why Google’s front page is no minimal, I think it was because we were doing this.

Since then I’ve consulted on the user experience design of dozens, maybe hundreds of web sites. Here’s one for credit.com, who were fantastic clients a couple of years ago.

I sat out the first dotcom crash writing a book based on the work I had been doing. It ’s a cookbook of user research methods. It came out in 2003 and the second edition [CLICK] will come out this fall.

And 2001 I co-founded a design and consulting company called Adaptive Path.

I left the Web behind in 2004 and founded a company with Tod E. Kurt called ThingM in 2006.

ThingM is a micro-OEM and an R&amp;D lab. We design and manufacture a range of smart LEDs for architects, industrial designers and hackers. Our products appear on everything from flying robots to Lady Gaga’s stage show. This is an RFID wine rack that we did about four years ago. The different light colors represent different facets of information that’s pulled down from a cloud-based service, such as current market price. This is a capacitive sensing kitchen cabinet knob we did two years ago. It glows when you touch it to creates a little bit of magic in your everyday environment and was an exploration in making a digital product that would still be useful 20 years after it was made.

In 2010 I wrote a book on the user experience design of ubiquitous computing devices, which I define as things that do information processing and networking, but are not experienced as general purpose computing or communication devices.

I also organize an annual summit of people developing hardware design tools for non-engineers.

However, ThingM, books and conferences are not my day job. They’re entertaining sidelines. My primary day job is as an innovation and user experience design consultant focusing on the design of digital consumer products. Here are some I’ve worked on for Yamaha, Whirlpool and Qualcomm.

The last couple of years my clients have been large consumer electronics companies. I’ve helped them design new innovative products and services and to create more user centered company cultures. I can’t give you any details.

I am honored that this is my third time to speak at Dorkbot. The first time I spoke about how magic was going to become a metaphor in how ubiquitous computing user experiences are presented. That digital products will increasingly emulate the functionality of mythological devices to explain how they work.

And in 2010 I talked about treating information as a kind of material to design with. I talked about how information processing had become cheap enough that you could think about it in the same way you choose one material over another, and that it could be evaluated in that way based on the properties it possesses, such as the ability to react to the world and to exhibit behavior.

Let me start with a small history lesson. Now this is only barely a history lesson, since I’m not a historian and I have no hard data, so please excuse me if this is totally wrong, but it’s my mental model for understanding the history of manufacturing efficiency. If you look at how many things you could produce from one unit of work, you see an interesting curve. For most of the last ten thousand or however many years, when you put one unit of work into a project, you got roughly one thing out of it. I realize those terms are somewhat imprecise, but bear with me. Sure, you got some gains in efficiency through tools like the potter’s wheel, the plow, the horse, the lever, fire, but those efficiencies were, roughly speaking linear. Then this thing happens. James Watt’s patent on the improvement to Newcomen’s steam engine expires in 1800. Boom. Exponential growth in the efficiency of production as anyone can take advantage of this new more efficient way of using energy. The Industrial Revolution happens at the beginning of the 19th century. That’s followed by steady increases in efficiency until we get to today’s industrial society. OK, that’s fairly familiar. Now, let’s look at a related curve, the number of units of work to make the FIRST thing. For most of history, that’s about one unit of work. Making the first thing of any set is hard. You become efficient later on, but the first time, you’re not going to be very efficient. And what’s worse, the more efficient we got at making many things, the harder it became to make the first thing. Mass produced objects are really complex, they require you to make the tools that make the tools that make the end product. It’s no longer a process that a single person can even afford to do time, money, or knowlege-wise. It requires a lot of expertise to be consolidated into a single geographic location. And then this other thing happens. In 2009 Stratasys’ patents on computer controlled additive manufacturing expire. Boom. The cost of making the first thing starts to plummet while the cost of making lots of things stays the same. That is where all of this comes from.

Imagine Amazon 8 years from now. It looks like this. Yes, it looks exactly like the Amazon today. It has all of the familiar ways to discover new products, to compare them, to see what people think of them, to see what goes with what. It has wish lists, Gold Boxes, the whole thing. But there’s a crucial difference. Instead of Amazon being the front end to a fulfillment system, as it is today, the Amazon of 2020 is the front end to a set of factories. The back end didn’t look like UPS, but Ford Motor Company. When you click on on buy you start a manufacturing process at the factory nearest you, instead of a delivery process from a warehouse far away.

I know what you’re thinking: “Mike just saw a MakerBot and got all excited. We’ve heard this all before, it’s called mass customization, and it’s never worked out.” Why talk about this again? Because I think that the presentation of mass customization as “configurators for everything” (such as this 1998 project from Levi’s) missed the point. That totally gets the user motivation wrong: most people don’t want to be designers of everything, they want to design a couple of things, but be consumers of the rest. Some people want to make their own clothes, but those people typically don’t build their own cars, and vice versa. Most people have better things to do than figure out what colors and patterns look good together in every situation, what is in fashion, what functionality they need to include in the firmware. They’re busy. They want someone who is a professional to do that research, to think really hard about what they need, to be really fluent in the tools that make it good, then to create a solution.

I’m also not talking about desktop manufacturing. As much as all us geeks want a Star Trek replicator, it’s not that useful in practice. We just don’t need that much new stuff all the time. Paper printers are useful because they represent high density information that fits into a rich existing culture of information use, and even they’re not used nearly as much as ecommerce sites. Outside of work, people probably shop a lot more than they print.

I think more importantly, both mass customization and desktop fabrication imagine a new world that’s different than ours. I have nothing against envisioning new worlds and working toward their creation, but creating new worlds, changing the behavior of millions of people, is really hard and takes a really long time. If we look to a world 8 years into the future, odds are that it’s not going to have changed that much, the odds are that most of us are not going to have a whole bunch more time on our hands to become mechanical engineers, electrical engineers, software engineers, and material scientists, as much as we’d like to. Makerbot photo by Scott Beale

2020 will actually probably look and works exactly like our world today, when seen from the outside. It’s still driven by the thrill of finding something awesome when you’re bored surfing the Internet and then making it yours by buying it. The relationship between the consumer and designer are still intact. Designers still design, ecommerce sites still help people find stuff they like, people still buy. However, there will be a crucial difference behind the scenes, and it will be this difference that changes our world from one of centralized warehouses to a world of distributed factories.

The difference is analytics. When you order from the Amazon of 2020 a counter is incremented that registers that you, a human being with a set of well-known behaviors and a demographic background, decided to buy this specific version of this specific idea. Moreover, since the world of 2020 is a world of ubiquitous computing, every product has a small bit of digital hardware in it that tracks how the product is used and, with your implicit permission, sends that information back to a central server, which aggregates and anonymizes the results. This is of course exactly how large-scale Web design works right now, but now we map it to all products.

Every design decision is the result of hypotheses about people’s behavior, preferences and interests, tested by observing actual behavior with competing designs that are rapidly deployed and whose use is closely monitored. Big web sites embed the scientific process of hypothesis generation, testing and validation into nearly every design decision. This is probably the most scientific design process in history. This diagram shows really big changes, but in reality the changes are tiny. It’s what Google was doing when that entire “43 shades of blue” controversy came out, which was really just them taking a useful tool too far.

When this approach couple analytics with all digital manufacturing technologies—not just additive, but CNC machines, laser sintering, Makerbots, etc.—we see a new way of creating products. When you have rapid, cheap, distributed manufacturing capability AND real-time analytics you have a new way of designing products. You can take those Industrial Age design processes that took years to react to people’s buying patterns, which took years to test hypotheses, and you can speed them up by orders of magnitude. Image: http://commons.wikimedia.org/wiki/File:Tailfins-evolution-1957-1959.jpg

Because it’s not like there aren’t ways to iterate on ideas using traditional manufacturing methods. Big food companies do it all the time, often by targeting regions which is probably why you’ve never had Gatorade Natural or Pepsi Ice, since they’re test market products that didn’t sell well enough to justify selling them here.

This tight loop between an idea and market validation of that idea is also the core of the Lean Startup philosophy that has been so successful in creating a wide variety of Web products and mobile apps over the last several years. The core concept is that you use the lightest weight method you possibly can to test an idea in the marketplace, that you make as little new technology as you possibly can to test a hypothesis. This is a slide from Steve Blank’s 2008 on customer development that illustrates this basic idea. My vision--MY hypothesis--is that it’s possible to do this with ANYTHING, but applying the ideas, practices and technologies we developed for the Net to everything else.

We’re all pretty familiar with one key component of this new ecosystem, which is low volume, fast, digital manufacturing. I realize that we’re just starting to talk about these technologies, but let’s start by assuming we have them, and we can express our hypotheses in atoms. Image: Dave Mellis.

The next piece is hypothesis testing. Well, that component is also coming online. Kickstarter, in this instance, is a kind of catalog for products that don&apos;t exist yet. It exists to give developers feedback about the popularity of their idea and teaches them how to position it for a market before they’ve made a single product. It provides two kinds of hypothesis testing: do people even want your idea, and what do they say they want it for.

Etsy allows very small run electronic products (as long as they’re made of felt).

Even fab.com, which sells limited-edition high design products like rugs and backpacks, sells small run electronics. These hypothesis-testing channels are immature, but they’re becoming increasingly popular. In effect, they’re doing an end run around the traditional consumer electronic sales channels and giving developers direct access to their customers in small, short bursts, letting them validate their ideas directly. This is bringing us closer to what we’ve become accustomed to when deploying software on the Web.

The key missing piece we still need to borrow from software is distributed collaborative design tools. To make better hypotheses we need to be able to take advantage of all of those specialized skills—all the different kinds of engineering—wherever they are, and to work together to create a shared understanding of what that hypothesis, that product, is. These tools are also coming online, though they’re still in their infancy. Commercial CAD systems are huge and incredibly difficult to learn. Product Lifecycle Management systems assume that you’re always building a commercial airplane, and are also insanely complex. When they mature, this is going to open the creative possibilities immensely.

Github got to where it is through an evolution of tools and practices that began with makefiles. The physical world isn’t even at the makefile stage.

To me, the whole ecosystem looks like this. Here come the buzz words, so excuse me in advance. Digital fabrication, we know what that is. It will allow us to make all kinds of things in small batches. Ubiquitous Computing and the Internet of Things is leading to everyday objects that send a stream of telemetry when we bring them home. They have an information shadow in the cloud that can be data mined. Big Data Analytics crunches all of that data to create information about people’s behavior. Social commerce creates sales channels that sell small numbers of products by finding niche markets and letting them market to each other. Kickstarter is currently a social commerce catalog for stuff that doesn’t exist until enough people want it. And finally, cloud-based design tools will allow designers and engineers to collaborate on the distributed development of physical products. And I don’t just mean cloud IDEs, I mean tools like Dave’s Arduino hack last year that connect physical prototypes to cloud-based collaborative systems. This is my ecosystem vision: a world where design directly drives product creation, and where data informs design. This is a world where products are made in small numbers only when they are requested. They are made locally, with local materials and workers, while at the same time being able to use design and engineering talent from anywhere on earth. In other words, they use the best qualities of both atoms and bits: atoms are available everywhere, bits travel fast. Designers in this vision add hypotheses against the actual market to their toolbox of design methods. In the full pipe dream, this means we use fewer natural resources, take full advantage of talented people wherever they are, create only products in large quantities that people need and want, meet the needs of tiny niche audiences, while still taking advantage of the infinite variety implicit in digital manufacturing technologies. Whew!

I intend to make this vision my next focus as a designer and entrepreneur. We just did the first iteration on Kickstarter of a product we hope will become different and more interesting as we iterate on it. However, I don’t expect that we will be able to do all of this by ourselves. I need your help: tell me what I don’t know, where I’m wrong. Tell me who I should talk to and where the opportunities are. I think this will change the world. I want to change the world. Interested? Talk to me.

Thank you.

Transcript

1.
THE NEW PRODUCTDEVELOPMENT ECOSYSTEMHOW WE WILL SKETCHPRODUCTS AND REINVENTMANUFACTURING IN THEPROCESS Mike Kuniavsky August 15, 2012 Dorkbot SF

2.
DORKBOT 2006: THE COMING AGE OF MAGIC This is from a patent Sony filed for a game controller. The Wii controller is another obviously similar device, but there are already many wandlike devices. Heres (Hitachi) Hitachis Magic Wand, and most of you will recognize this bit of necromancy (airport). Sony magic wand patent

3.
DORKBOT 2010: INFORMATION AS A MATERIAL Cheap processing also creates the opportunity to use information as a decorative material. A lot of data visualization today is as much about decoration as it is about information analysis or communication, and that trend is only going to continue. Information is no different a material than any other material. Wood can hold up a house, or you can make a sculpture with it. Information can be used to create incredibly beautiful, profound esthetic experiences. It has already revolutionized music and cinema, but treating as a permanent material, rather than a medium, creates fantastic new opportunities.!